Source code for the conference paper: Xu, A., Patel, D., Lin, W., & Luo, P. (2025, July 16–18). Predicting T cell receptor specificity with graph attention networks. 12th International Work-Conference on Bioinformatics and Biomedical Engineering, Gran Canaria, Spain, Accepted.
T cells play a pivotal role in adaptive immunity by recognizing a vast array of antigens through their T cell receptors (TCRs). Advances in high-throughput sequencing have enabled unprecedented profiling of TCR repertoires, enabling the computationally prediction of TCR-antigen associations. Existing machine learning approaches, including deep learning and clustering algorithms, have shown promise in identifying patterns within TCR sequences, particularly through features such as complementarity-determining region 3 (CDR3) sequences and V(D)J gene usage. However, many existing models are constrained by their architectural limitations, and often relying on feature concatenation to achieve multi-modal analysis.
In this study, we proposed an algorithm that integrates deep learning techniques with immune repertoire data to enhance TCR specificity prediction. By leveraging language models and graph attention networks, our framework systematically identifies patterns that correlate with antigen recognition, improving predictive accuracy and enhancing multi-modal analysis of TCR sequencing data. Experiment results obtained from two public datasets showed that our method can accurately predict TCR-epitope associations. Our study also underscores the importance of integrating biomolecular networks and advanced language models to uncover fundamental principles of immune responses and translate them into clinically relevant applications.
